PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection

Abstract

Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP.

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

“…YOLO v8 achieves the highest precision, the mAP and F1-scores were 75.3% and 0.732, respectively, while the YOLO v5 model has the second highest accuracy, indicating that the YOLO series models have strong detection capabilities compared to the other modules. Although there is limited research on YOLO v8, the relevant studies also confirmed the superiority of the recently released YOLO series in object detection [58][59][60][61].…”

Detection Transformer with Multi-Scale Fusion Attention Mechanism for Aero-Engine Turbine Blade Cast Defect Detection Considering Comprehensive Features

“…This technique extracts cross-modal discriminant information, thereby improving the accuracy of detecting small targets. Moreover, Liu et al [ 31 ] enhanced YOLOv7, integrated a new convolution operator into the backbone network, and extended the sensitivity field; thus, they reduced the parameters by 18.03% and computed the load of the defect detection model that was equal to 20.53%. In addition, Zhu et al [ 32 ] proposed a variety of SSD-based modules to detect concrete cracks, where the feature fusion-enhanced module can effectively fuse the feature channel information to enhance object detection precision.…”